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We discuss Bayesian model uncertainty analysis and forecasting in sequentialdynamic modeling of multivariate time series. The perspective is that of adecision-maker with a specific forecasting objective that guides thinking aboutrelevant models. Based on formal Bayesian decision-theoretic reasoning, wedevelop a time-adaptive approach to exploring, weighting, combining andselecting models that differ in terms of predictive variables included. Theadaptivity allows for changes in the sets of favored models over time, and isguided by the specific forecasting goals. A synthetic example illustrates howdecision-guided variable selection differs from traditional Bayesian modeluncertainty analysis and standard model averaging. An applied study in onemotivating application of long-term macroeconomic forecasting highlights theutility of the new approach in terms of improving predictions as well as itsability to identify and interpret different sets of relevant models over timewith respect to specific, defined forecasting goals.

Adaptive Variable Selection for Sequential Prediction in Multivariate Dynamic Models